GCSENet: A GCN, CNN and SENet ensemble model for microRNA-disease association prediction

Recently, an increasing number of studies have demonstrated that miRNAs are involved in human diseases, indicating that miRNAs might be a potential pathogenic factor for various diseases. Therefore, figuring out the relationship between miRNAs and diseases plays a critical role in not only the development of new drugs, but also the formulation of individualized diagnosis and treatment. As the prediction of miRNA-disease association via biological experiments is expensive and time-consuming, computational methods have a positive effect on revealing the association. In this study, a novel prediction model integrating GCN, CNN and Squeeze-and-Excitation Networks (GCSENet) was constructed for the identification of miRNA-disease association. The model first captured features by GCN based on a heterogeneous graph including diseases, genes and miRNAs. Then, considering the different effects of genes on each type of miRNA and disease, as well as the different effects of the miRNA-gene and disease-gene relationships on miRNA-disease association, a feature weight was set and a combination of miRNA-gene and disease-gene associations was added as feature input for the convolution operation in CNN. Furthermore, the squeeze and excitation blocks of SENet were applied to determine the importance of each feature channel and enhance useful features by means of the attention mechanism, thus achieving a satisfactory prediction of miRNA-disease association. The proposed method was compared against other state-of-the-art methods. It achieved an AUROC score of 95.02% and an AUPR score of 95.55% in a 10-fold cross-validation, which led to the finding that the proposed method is superior to these popular methods on most of the performance evaluation indexes.Author summary: Identifying miRNA-disease associations accelerates the understanding towards pathogenicity, which is beneficial for the development of treatment tools for diseases. Different from existing methods, our GCSENet captures the deep relationship between miRNA and disease through three heterogeneous graphs (disease, gene and miRNA) to promote an accurate prediction result. We performed the 10-fold cross validation to evaluate the performance of GCSENet, which can outperform many classic methods. Furthermore, we carried out case studies on four important diseases, which were used to evaluate the performance of our model regarding to the associations with experimental evidences in literature. The result shows that most predicted miRNAs (48 for lung neoplasms, 48 for heart failure, 48 for breast cancer and 50 for glioblastoma) in the top 50 predictions were confirmed in HMDD v3.0. As a result, it shows that GCSENet can make reliable predictions and guide experiments to uncover more miRNA-disease associations.